Source code for monai.utils.misc

# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

import itertools
import random
from ast import literal_eval
from distutils.util import strtobool
from typing import Any, Callable, Optional, Sequence, Tuple, Union

import numpy as np
import torch

__all__ = [

_seed = None
_flag_deterministic = torch.backends.cudnn.deterministic
_flag_cudnn_benchmark = torch.backends.cudnn.benchmark
MAX_SEED = np.iinfo(np.uint32).max + 1  # 2**32, the actual seed should be in [0, MAX_SEED - 1] for uint32

[docs]def zip_with(op, *vals, mapfunc=map): """ Map `op`, using `mapfunc`, to each tuple derived from zipping the iterables in `vals`. """ return mapfunc(op, zip(*vals))
[docs]def star_zip_with(op, *vals): """ Use starmap as the mapping function in zipWith. """ return zip_with(op, *vals, mapfunc=itertools.starmap)
[docs]def first(iterable, default=None): """ Returns the first item in the given iterable or `default` if empty, meaningful mostly with 'for' expressions. """ for i in iterable: return i return default
[docs]def issequenceiterable(obj: Any) -> bool: """ Determine if the object is an iterable sequence and is not a string. """ if torch.is_tensor(obj): return int(obj.dim()) > 0 # a 0-d tensor is not iterable return isinstance(obj, and not isinstance(obj, str)
[docs]def ensure_tuple(vals: Any) -> Tuple[Any, ...]: """ Returns a tuple of `vals`. """ if not issequenceiterable(vals): vals = (vals,) return tuple(vals)
[docs]def ensure_tuple_size(tup: Any, dim: int, pad_val: Any = 0) -> Tuple[Any, ...]: """ Returns a copy of `tup` with `dim` values by either shortened or padded with `pad_val` as necessary. """ tup = ensure_tuple(tup) + (pad_val,) * dim return tuple(tup[:dim])
[docs]def ensure_tuple_rep(tup: Any, dim: int) -> Tuple[Any, ...]: """ Returns a copy of `tup` with `dim` values by either shortened or duplicated input. Raises: ValueError: When ``tup`` is a sequence and ``tup`` length is not ``dim``. Examples:: >>> ensure_tuple_rep(1, 3) (1, 1, 1) >>> ensure_tuple_rep(None, 3) (None, None, None) >>> ensure_tuple_rep('test', 3) ('test', 'test', 'test') >>> ensure_tuple_rep([1, 2, 3], 3) (1, 2, 3) >>> ensure_tuple_rep(range(3), 3) (0, 1, 2) >>> ensure_tuple_rep([1, 2], 3) ValueError: Sequence must have length 3, got length 2. """ if not issequenceiterable(tup): return (tup,) * dim if len(tup) == dim: return tuple(tup) raise ValueError(f"Sequence must have length {dim}, got {len(tup)}.")
[docs]def fall_back_tuple(user_provided: Any, default: Sequence, func: Callable = lambda x: x and x > 0) -> Tuple[Any, ...]: """ Refine `user_provided` according to the `default`, and returns as a validated tuple. The validation is done for each element in `user_provided` using `func`. If `func(user_provided[idx])` returns False, the corresponding `default[idx]` will be used as the fallback. Typically used when `user_provided` is a tuple of window size provided by the user, `default` is defined by data, this function returns an updated `user_provided` with its non-positive components replaced by the corresponding components from `default`. Args: user_provided: item to be validated. default: a sequence used to provided the fallbacks. func: a Callable to validate every components of `user_provided`. Examples:: >>> fall_back_tuple((1, 2), (32, 32)) (1, 2) >>> fall_back_tuple(None, (32, 32)) (32, 32) >>> fall_back_tuple((-1, 10), (32, 32)) (32, 10) >>> fall_back_tuple((-1, None), (32, 32)) (32, 32) >>> fall_back_tuple((1, None), (32, 32)) (1, 32) >>> fall_back_tuple(0, (32, 32)) (32, 32) >>> fall_back_tuple(range(3), (32, 64, 48)) (32, 1, 2) >>> fall_back_tuple([0], (32, 32)) ValueError: Sequence must have length 2, got length 1. """ ndim = len(default) user = ensure_tuple_rep(user_provided, ndim) return tuple( # use the default values if user provided is not valid user_c if func(user_c) else default_c for default_c, user_c in zip(default, user) )
def is_scalar_tensor(val: Any) -> bool: if torch.is_tensor(val) and val.ndim == 0: return True return False def is_scalar(val: Any) -> bool: if torch.is_tensor(val) and val.ndim == 0: return True return bool(np.isscalar(val))
[docs]def progress_bar(index: int, count: int, desc: Optional[str] = None, bar_len: int = 30, newline: bool = False) -> None: """print a progress bar to track some time consuming task. Args: index: current status in progress. count: total steps of the progress. desc: description of the progress bar, if not None, show before the progress bar. bar_len: the total length of the bar on screen, default is 30 char. newline: whether to print in a new line for every index. """ end = "\r" if newline is False else "\r\n" filled_len = int(bar_len * index // count) bar = f"{desc} " if desc is not None else "" bar += "[" + "=" * filled_len + " " * (bar_len - filled_len) + "]" print(f"{index}/{count} {bar}", end=end) if index == count: print("")
def get_seed() -> Optional[int]: return _seed
[docs]def set_determinism( seed: Optional[int] = np.iinfo(np.uint32).max, additional_settings: Optional[Union[Sequence[Callable[[int], Any]], Callable[[int], Any]]] = None, ) -> None: """ Set random seed for modules to enable or disable deterministic training. Args: seed: the random seed to use, default is np.iinfo(np.int32).max. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed. if set to None, will disable deterministic training. additional_settings: additional settings that need to set random seed. """ if seed is None: # cast to 32 bit seed for CUDA seed_ = torch.default_generator.seed() % (np.iinfo(np.int32).max + 1) if not torch.cuda._is_in_bad_fork(): torch.cuda.manual_seed_all(seed_) else: seed = int(seed) % MAX_SEED torch.manual_seed(seed) global _seed _seed = seed random.seed(seed) np.random.seed(seed) if additional_settings is not None: additional_settings = ensure_tuple(additional_settings) for func in additional_settings: func(seed) if seed is not None: torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False else: # restore the original flags torch.backends.cudnn.deterministic = _flag_deterministic torch.backends.cudnn.benchmark = _flag_cudnn_benchmark
[docs]def list_to_dict(items): """ To convert a list of "key=value" pairs into a dictionary. For examples: items: `["a=1", "b=2", "c=3"]`, return: {"a": "1", "b": "2", "c": "3"}. If no "=" in the pair, use None as the value, for example: ["a"], return: {"a": None}. Note that it will remove the blanks around keys and values. """ def _parse_var(s): items = s.split("=", maxsplit=1) key = items[0].strip(" \n\r\t'") value = None if len(items) > 1: value = items[1].strip(" \n\r\t'") return key, value d = {} if items: for item in items: key, value = _parse_var(item) try: if key in d: raise KeyError(f"encounter duplicated key {key}.") d[key] = literal_eval(value) except ValueError: try: d[key] = bool(strtobool(str(value))) except ValueError: d[key] = value return d
_torch_to_np_dtype = { torch.bool: np.bool, torch.uint8: np.uint8, torch.int8: np.int8, torch.int16: np.int16, torch.int32: np.int32, torch.int64: np.int64, torch.float16: np.float16, torch.float32: np.float32, torch.float64: np.float64, torch.complex64: np.complex64, torch.complex128: np.complex128, } _np_to_torch_dtype = {value: key for key, value in _torch_to_np_dtype.items()}
[docs]def dtype_torch_to_numpy(dtype): """Convert a torch dtype to its numpy equivalent.""" return _torch_to_np_dtype[dtype]
[docs]def dtype_numpy_to_torch(dtype): """Convert a numpy dtype to its torch equivalent.""" return _np_to_torch_dtype[dtype]